Assessing clinical significance using robust normative comparisons
Bibliographic record
Abstract
OBJECTIVE: Clinical significance determines whether an intervention makes a real difference in the everyday life of a client. One of the most recommended approaches for conducting group-level analyses of clinical significance is to evaluate whether the treated clinical group is equivalent to a normal comparison group (normative comparisons). The purpose of this study was to demonstrate the analytical and practical power of assessing clinical significance using normative comparisons that are robust to violations of normality and homogeneity of variance assumptions. METHOD: Six datasets were gleaned from published intervention studies for depression. RESULTS: We found that normative comparisons using a robust Schuirmann-Yuen test determined equivalency for 11% fewer clinical samples compared to original normative comparisons that use a Schuirmann test of equivalence. CONCLUSIONS: We recommend that researchers conducting normative comparisons utilize the Schuirmann-Yuen procedure as it provides the most reliable method available for determining if a treated clinical group is equivalent to a normative comparison group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.175 | 0.466 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".